{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting torch==2.2\n",
      "  Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl.metadata (25 kB)\n",
      "Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (4.9.0)\n",
      "Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (1.12)\n",
      "Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2.8.5)\n",
      "Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (3.1.3)\n",
      "Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch==2.2) (2024.2.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->torch==2.2) (2.1.5)\n",
      "Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch==2.2) (1.3.0)\n",
      "Using cached torch-2.2.0-cp39-none-macosx_11_0_arm64.whl (59.7 MB)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0mInstalling collected packages: torch\n",
      "  Attempting uninstall: torch\n",
      "    Found existing installation: torch 1.11.0\n",
      "    Uninstalling torch-1.11.0:\n",
      "      Successfully uninstalled torch-1.11.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "torchdata 0.4.1 requires torch==1.12.1, but you have torch 2.2.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed torch-2.2.0\n",
      "Requirement already satisfied: fastai==2.7.14 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (2.7.14)\n",
      "Requirement already satisfied: pip in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (23.3.1)\n",
      "Requirement already satisfied: packaging in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (23.2)\n",
      "Requirement already satisfied: fastdownload<2,>=0.0.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (0.0.7)\n",
      "Requirement already satisfied: fastcore<1.6,>=1.5.29 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.5.29)\n",
      "Requirement already satisfied: torchvision>=0.11 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (0.14.1)\n",
      "Requirement already satisfied: matplotlib in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (3.5.2)\n",
      "Requirement already satisfied: pandas in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.2.0)\n",
      "Requirement already satisfied: requests in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.31.0)\n",
      "Requirement already satisfied: pyyaml in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (6.0.1)\n",
      "Requirement already satisfied: fastprogress>=0.2.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.0.3)\n",
      "Requirement already satisfied: pillow>=9.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (9.3.0)\n",
      "Requirement already satisfied: scikit-learn in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.4.1.post1)\n",
      "Requirement already satisfied: scipy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (1.12.0)\n",
      "Requirement already satisfied: spacy<4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (3.7.4)\n",
      "Requirement already satisfied: torch<2.3,>=1.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from fastai==2.7.14) (2.2.0)\n",
      "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.0.12)\n",
      "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.0.5)\n",
      "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.0.10)\n",
      "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.0.8)\n",
      "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.0.9)\n",
      "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (8.2.3)\n",
      "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.1.2)\n",
      "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.4.8)\n",
      "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.0.10)\n",
      "Requirement already satisfied: weasel<0.4.0,>=0.1.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (0.3.4)\n",
      "Requirement already satisfied: typer<0.10.0,>=0.3.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (0.9.0)\n",
      "Requirement already satisfied: smart-open<7.0.0,>=5.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (6.4.0)\n",
      "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (4.66.2)\n",
      "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (2.6.1)\n",
      "Requirement already satisfied: jinja2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.1.3)\n",
      "Requirement already satisfied: setuptools in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (68.2.2)\n",
      "Requirement already satisfied: langcodes<4.0.0,>=3.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (3.3.0)\n",
      "Requirement already satisfied: numpy>=1.19.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from spacy<4->fastai==2.7.14) (1.26.4)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (3.3.2)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (3.6)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (2.2.1)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from requests->fastai==2.7.14) (2024.2.2)\n",
      "Requirement already satisfied: filelock in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (3.13.1)\n",
      "Requirement already satisfied: typing-extensions>=4.8.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (4.9.0)\n",
      "Requirement already satisfied: sympy in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (1.12)\n",
      "Requirement already satisfied: networkx in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (2.8.5)\n",
      "Requirement already satisfied: fsspec in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from torch<2.3,>=1.10->fastai==2.7.14) (2024.2.0)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (4.49.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (1.4.5)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (3.1.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from matplotlib->fastai==2.7.14) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pandas->fastai==2.7.14) (2024.1)\n",
      "Requirement already satisfied: tzdata>=2022.7 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pandas->fastai==2.7.14) (2024.1)\n",
      "Requirement already satisfied: joblib>=1.2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-learn->fastai==2.7.14) (1.3.2)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from scikit-learn->fastai==2.7.14) (3.3.0)\n",
      "Requirement already satisfied: annotated-types>=0.4.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai==2.7.14) (0.6.0)\n",
      "Requirement already satisfied: pydantic-core==2.16.2 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4->spacy<4->fastai==2.7.14) (2.16.2)\n",
      "Requirement already satisfied: six>=1.5 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from python-dateutil>=2.7->matplotlib->fastai==2.7.14) (1.16.0)\n",
      "Requirement already satisfied: blis<0.8.0,>=0.7.8 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from thinc<8.3.0,>=8.2.2->spacy<4->fastai==2.7.14) (0.7.11)\n",
      "Requirement already satisfied: confection<1.0.0,>=0.0.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from thinc<8.3.0,>=8.2.2->spacy<4->fastai==2.7.14) (0.1.4)\n",
      "Requirement already satisfied: click<9.0.0,>=7.1.1 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from typer<0.10.0,>=0.3.0->spacy<4->fastai==2.7.14) (8.1.7)\n",
      "Requirement already satisfied: cloudpathlib<0.17.0,>=0.7.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from weasel<0.4.0,>=0.1.0->spacy<4->fastai==2.7.14) (0.16.0)\n",
      "Requirement already satisfied: MarkupSafe>=2.0 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from jinja2->spacy<4->fastai==2.7.14) (2.1.5)\n",
      "Requirement already satisfied: mpmath>=0.19 in /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages (from sympy->torch<2.3,>=1.10->fastai==2.7.14) (1.3.0)\n",
      "\u001b[33mDEPRECATION: pytorch-lightning 1.7.0 has a non-standard dependency specifier torch>=1.9.*. pip 24.0 will enforce this behaviour change. A possible replacement is to upgrade to a newer version of pytorch-lightning or contact the author to suggest that they release a version with a conforming dependency specifiers. Discussion can be found at https://github.com/pypa/pip/issues/12063\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install torch==2.2\n",
    "!pip install fastai==2.7.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/io/image.py:13: UserWarning: Failed to load image Python extension: dlopen(/Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/image.so, 0x0006): Symbol not found: __ZN3c1017RegisterOperatorsD1Ev\n",
      "  Referenced from: <3F789787-FE38-3CE7-8599-064BDD0416EE> /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torchvision/image.so\n",
      "  Expected in:     <ADC0A61A-5B83-3A02-975F-EE5DFF441305> /Users/Ashish.Jha/anaconda3/envs/python39/lib/python3.9/site-packages/torch/lib/libtorch_cpu.dylib\n",
      "  warn(f\"Failed to load image Python extension: {e}\")\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from fastai.vision.all import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/Ashish.Jha/.fastai/data/mnist_png\n"
     ]
    }
   ],
   "source": [
    "path = untar_data(URLs.MNIST)\n",
    "print(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60000\n",
      "/Users/Ashish.Jha/.fastai/data/mnist_png/training/9/36655.png\n"
     ]
    }
   ],
   "source": [
    "files = get_image_files(path/\"training\")\n",
    "print(len(files))\n",
    "print(files[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def label_func(f): return f.parent.name\n",
    "dls = ImageDataLoaders.from_path_func(path, fnames=files, label_func=label_func, num_workers=0)\n",
    "dls.show_batch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn = vision_learner(dls, arch=resnet18, metrics=accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SuggestedLRs(valley=0.002511886414140463)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</style>\n"
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      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>time</th>\n",
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   "source": [
    "learn.fine_tune(epochs=2, base_lr=0.0012, freeze_epochs=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
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       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
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       "</style>\n"
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    {
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",
      "text/plain": [
       "<Figure size 900x900 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.show_results()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
       "        /* gets rid of default border in Firefox and Opera. */\n",
       "        border: none;\n",
       "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "        background-size: auto;\n",
       "    }\n",
       "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
       "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
       "    }\n",
       "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "        background: #F44336;\n",
       "    }\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp = Interpretation.from_learner(learn)\n",
    "interp.plot_top_losses(9, figsize=(15,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
